Sustainable Machine Learning on Edge Device Clusters (Sommersemester 2020)
Dozent:
Dr. Thorsten Papenbrock
(Information Systems)
,
Phillip Wenig
(Information Systems)
Website zum Kurs:
https://hpi.de/naumann/teaching/teaching/ss-20/sustainable-machine-learning-on-edge-device-clusters.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 06.04.2020 - 22.04.2020
- Lehrform: Projekt / Seminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
- Maximale Teilnehmerzahl: 8
Studiengänge, Modulgruppen & Module
- IT-Systems Engineering
- IT-Systems Engineering
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
- SCAL: Scalable Data Systems
- HPI-SCAL-K Konzepte und Methode
- SCAL: Scalable Data Systems
- HPI-SCAL-T echniken und Werkzeuge
- SCAL: Scalable Data Systems
- HPI-SCAL-S Spezialisierung
Beschreibung
In this project seminar, we develop a prototype system that runs end-to-end machine learning pipelines on clusters of edge devices with limited compute power to encourage sustainable hardware usage. For more details about the project and the seminar setup, please have a look at our official project website.
IMPORTANT NOTE: Due to the current COVID-19 situation, we need to start this seminar in online-mode. This means that we use jitsi web-meetings for on-boarding and our regular group sessions. Please find the detailes about our organization on our official project website.
Voraussetzungen
All participants need to be familiar with the actor programming model and the Akka toolkit. At best, you have already taken the lecture Distributed Data Management; otherwise, you need to catch up on actor programming.
Leistungserfassung
The grading will be based on the following tasks:
- (10%) Active participation during all seminar events.
- (60%) Research and development success w.r.t. your pipeline modules including:
- (20%) Implementation
- (20%) Evaluation
- (20%) Paper writing (~1.5 pages per person)
- (30%) Presentations including:
- (15%) Midterm presentation
- (15%) Final presentation
Termine
- Weekly meetings
- Time: Tuesdays, 9:15 - 10:45 AM
- Location: F-2.10, Building F, 2nd Floor, Campus II
Online meetings during the COVID-19 lockdown.
Zurück
